A Class of Submodular Functions for Document Summarization

By Hui-Ching Lin, J. Bilmes. 2011

863 citations recorded by the source corpus.

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Citations in the corpus

Learning to Extract Coherent Summary via Deep Reinforcement Learning (2018, methodology): “Traditional methods mainly focus on scoring sentences using graph-based method (Erkan and Radev 2004), submodular functions (Lin and Bilmes 2011) or integer linear programming (Berg-Kirkpatrick, Gillick, and Klein 2011), which are coupled with handcrafted features.”
Is Attention Interpretable? (2019, background): “…use reinforcement learning to attempt to construct such a minimal set of words, Lei et al. (2016) train an encoder to constrain the input prior to classification, and much of the work that has been done on extractive summarization takes this concept as a starting point (Lin and Bilmes, 2011).”
Learning-Based Single-Document Summarization with Compression and Anaphoricity Constraints (2016, background): “While multi-document summarization is well-studied in the NLP literature (Carbonell and Gold-stein, 1998; Gillick and Favre, 2009; Lin and Bilmes, 2011; Nenkova and McKeown, 2011), single-document summarization (McKeown et al., 1995; Marcu, 1998; Mani, 2001; Hirao et al., 2013) has received less…”
Neural Document Summarization by Jointly Learning to Score and Select Sentences (2018, methodology): “Lin and Bilmes (2011) propose using submodular functions to ﬁnd the subset.”
Learning with Submodular Functions: A Convex Optimization Perspective (2011, background): “We are by no means exhaustive and other applications may be found in facility location [50, 51, 1], game theory [66], document summarization [132], social networks [118], or clustering [159].”
Domain and Function: A Dual-Space Model of Semantic Relations and Compositions (2012, background): “Recent work (Lin & Bilmes, 2011) has shown that argmax problems can be solved efficiently and effectively if they can be framed as monotone submodular function maximization problems.”
Examples are not enough, learn to criticize! Criticism for Interpretability (2016, background): “Progress has included novel cost functions and algorithms for several domains including image summarization (Simon et al., 2007) and document summarizauion (Lin and Bilmes, 2011).”
Streaming submodular maximization: massive data summarization on the fly (2014, background): “, [17, 22, 10, 9, 23, 33, 30]), which can potentially benefit from our work.”
Online Submodular Maximization with Preemption (2015, methodology): “In machine learning, maximization of submodular functions has been used for document summarization [46, 47], sensor placement [41, 42, 44], and information gathering [43].”
Implicit Diversity in Image Summarization (2019, background): “Secondly, to ensure that the summary is representative of all relevant images, most prior work have used the idea of non-redundancy [14, 20, 53, 65, 70].”
Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints (2013, background): “This problem occurs naturally in a number of problems related to active/online learning [7] and summarization [21, 22].”
Submodular Maximization with Cardinality Constraints (2014, methodology): “The quality of the summary is a nonmonotone and non-negative submodular function as similarities between selected textual units are deducted from the total benefit these textual units contribute to the summary (the reader is referred to [41, 42] for more details).”
Reducing the Frequency of Hallucinated Quantities in Abstractive Summaries (2020, background): “…approaches are either extractive, selecting and assembling salient words, phrases and sentences from the source text to form the summary (Lin and Bilmes, 2011; Nallapati et al., 2017; Narayan et al., 2018b), or abstractive, generating the summary from scratch, containing novel words and phrases…”
Quantum machine learning: a classical perspective (2017, background): “Examples of such applications are very diverse, such as document summarization [175], social networks [176] or clustering [177] to name but a few.”
Recent advances in document summarization (2017, background): “Despite simple structures, they already achieve competitive performance on DUC datasets [117].”